class_docstr: Exponential [1] objective function.
  
  This class defines the Exponential global optimization problem. This
  is a multimodal minimization problem defined as follows:
  
  .. math::
  
      f_{\text{Exponential}}(x) = -e^{-0.5 \sum_{i=1}^n x_i^2}
  
  
  Here, :math:`n` represents the number of dimensions and :math:`x_i \in
  [-1, 1]` for :math:`i = 1, ..., n`.
  
  *Global optimum*: :math:`f(x_i) = -1` for :math:`x_i = 0` for
  :math:`i = 1, ..., n`
  
  .. [1] Jamil, M. & Yang, X.-S. A Literature Survey of Benchmark Functions
  For Global Optimization Problems Int. Journal of Mathematical Modelling
  and Numerical Optimisation, 2013, 4, 150-194.
  
  TODO Jamil are missing a minus sign on fglob
code: def __init__(self, dimensions=2):
      Benchmark.__init__(self, dimensions)
  
      self._bounds = list(zip([-1.0] * self.N, [1.0] * self.N))
  
      self.global_optimum = [[0.0 for _ in range(self.N)]]
      self.fglob = -1.0
      self.change_dimensionality = True
  
imports: from numpy import abs, asarray, cos, exp, arange, pi, sin, sqrt, sum
  from .go_benchmark import Benchmark
label_desc: Use image node_id 1 to create a new Exponential object from inherited base classes: Benchmark with example: obj = Exponential(dimensions)